Wednesday, January 9, 2013

How should one update global and regional estimates and maintain long-term homogeneity?

Prompted by recent discussions in various blogs and elsewhere (I'm writing this from a flaky airport connection on a laptop so no links - sorry) it seems that, for maybe the umpteenth time, there are questions about how the various current global and some national estimates are updated. Having worked in two organizations that take two very distinct approaches I thought it worth giving some perspective. It may also help inform how others who might come in and use the databank to create new products choose to approach the issue.

The fundamental issue of how to curate and update a global, regional or national product whilst maintaining homogeneity is a vexed one. Non-climatic artifacts are not the sole preserve of the historical portion of the station records. Still today stations move, instruments change, times of observation change etc. etc. often for very good and understandable reason (and often not ...). There is no obvious best way to deal with this issue. If ignored for long enough station, local and even regional series can become highly unrealistic if large very recent biases are not dealt with.

The problem is also intrinsically inter-linked with the question as to which period of the record we should adjust for non-climatic effects. Here, at least there is general agreement that adjustment should be made to match the most recent apparently homogeneous segment so that today's readings can be easily and readily compared to our estimates of past variability and change without performing mental gymnastics.

At one extreme of the set of approaches is the CRUTEM method. Here, real-time data updates are only made to a recent period (I think still just post-2000) and no explicit assessment of homogeneity is made at the monthly update granuality (there is QC applied). Rather adjustments and new pre-2000 data effectively are caught up with major releases or network updates (e.g. with entirely new station record additions / replacements / assessments normally associated with a version increment and manuscript). This ensures values prior to a recent decade or so remain static for most month to month updates but at a potential cost if a station inhomogeneity occurs in the recent past which is de facto unaccounted for. This can only then be caught up with through a substantive update.

At the other extreme is the approach undertaken in GHCN / USHCN. Here the entire network is reassessed based upon new data receipts every night using the automated homogenization algorithm. New modern periods of records can change the identification of recent breaks in stations that contribute to the network. Because the adjustments are time-invariant deltas applied to all points prior to an identified break the impact is to change values in the deep past to better match modern data. So, the addition of station data for Jan 2013 may change values estimated for Jan 1913 (or July 1913) because the algorithm now has enough data to find a break that occurred in 2009. This then may affect the nth significant figure of the national / global calculation in 1913 on a day to day basis. This is why with GHCNv3 a system of version control of v3.x.y.z.ddmmyyyy was introduced and each version archived. If you want bit replication to be possible of your analysis then explicitly reference the version you used.

What is the optimal solution? Perhaps this is a 'How long is a piece of string?' class of question. There are very obvious benefits to either approach or any number of others. In part it depends upon the intended uses of the product. If interested in serving homogeneous station series as well as aggregated area averaged series using your best knowledge as of today perhaps something closer to NCDC's approach. If interested mainly in large scale average determination and under a reasonable null that at least on a few years timescale the inevitable new systematic artifacts average out as gaussian over broad enough space scales the CRUTEM approach makes more sense. And that, perhaps, is fundamentally why they chose these different routes ...

2 comments:

Good post, and one of the most straight-forward discussions I've seen of the issue with adjustments due to new data affecting past records. I've been meaning to start playing around with ISTI data, and will probably do a simple comparison of ISTI/GHCN-M/Berkeley results using a basic common anomaly method / 5x5 lat-lon grid approach.